



'''simple docstring'''


from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
						import PIL


lowercase_   :					Dict        =     logging.get_logger(__name__)




class   __UpperCamelCase							(_UpperCAmelCase      ):
	__A      	=       ["""pixel_values"""]







	def __init__(     self			,					_lowerCAmelCase = True			,					_lowerCAmelCase = None			,					_lowerCAmelCase = None			,					_lowerCAmelCase = PILImageResampling.BILINEAR			,					_lowerCAmelCase = True			,					_lowerCAmelCase = 1 / 255			,					_lowerCAmelCase = True			,					_lowerCAmelCase = None			,					_lowerCAmelCase = None			,					**_lowerCAmelCase			,					)     ->     None:
							'''simple docstring'''






							super().__init__(**_lowerCAmelCase    )
							lowercase   =				size if size is not None else {'shortest_edge': 384}
							lowercase   =				get_size_dict(_lowerCAmelCase			,					default_to_square=_lowerCAmelCase    )

							lowercase   =				do_resize
							lowercase   =				size
							# Default value set here for backwards compatibility where the value in config is None
							lowercase   =				crop_pct if crop_pct is not None else 224 / 256
							lowercase   =				resample
							lowercase   =				do_rescale
							lowercase   =				rescale_factor
							lowercase   =				do_normalize
							lowercase   =				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							lowercase   =				image_std if image_std is not None else IMAGENET_STANDARD_STD







	def 	_a (     self			,					_lowerCAmelCase			,					_lowerCAmelCase			,					_lowerCAmelCase			,					_lowerCAmelCase = PILImageResampling.BICUBIC			,					_lowerCAmelCase = None			,					**_lowerCAmelCase			,					)     ->     np.ndarray:
							'''simple docstring'''






							lowercase   =				get_size_dict(_lowerCAmelCase			,					default_to_square=_lowerCAmelCase    )
							if "shortest_edge" not in size:
													raise ValueError(F"""Size dictionary must contain 'shortest_edge' key. Got {size.keys()}"""    )
							lowercase   =				size['shortest_edge']

							if shortest_edge < 384:
													# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
													lowercase   =				int(shortest_edge / crop_pct    )
													lowercase   =				get_resize_output_image_size(_lowerCAmelCase			,					size=_lowerCAmelCase			,					default_to_square=_lowerCAmelCase    )
													lowercase   =				resize(image=_lowerCAmelCase			,					size=_lowerCAmelCase			,					resample=_lowerCAmelCase			,					data_format=_lowerCAmelCase			,					**_lowerCAmelCase    )
													# then crop to (shortest_edge, shortest_edge)
													return center_crop(image=_lowerCAmelCase			,					size=(shortest_edge, shortest_edge)			,					data_format=_lowerCAmelCase			,					**_lowerCAmelCase    )
							else:
													# warping (no cropping) when evaluated at 384 or larger
													return resize(
													    _lowerCAmelCase			,					size=(shortest_edge, shortest_edge)			,					resample=_lowerCAmelCase			,					data_format=_lowerCAmelCase			,					**_lowerCAmelCase    )







	def 	_a (     self			,					_lowerCAmelCase			,					_lowerCAmelCase			,					_lowerCAmelCase = None			,					**_lowerCAmelCase			,					)     ->     Optional[int]:
							'''simple docstring'''






							return rescale(_lowerCAmelCase			,					scale=_lowerCAmelCase			,					data_format=_lowerCAmelCase			,					**_lowerCAmelCase    )







	def 	_a (     self			,					_lowerCAmelCase			,					_lowerCAmelCase			,					_lowerCAmelCase			,					_lowerCAmelCase = None			,					**_lowerCAmelCase			,					)     ->     np.ndarray:
							'''simple docstring'''






							return normalize(_lowerCAmelCase			,					mean=_lowerCAmelCase			,					std=_lowerCAmelCase			,					data_format=_lowerCAmelCase			,					**_lowerCAmelCase    )







	def 	_a (     self			,					_lowerCAmelCase			,					_lowerCAmelCase = None			,					_lowerCAmelCase = None			,					_lowerCAmelCase = None			,					_lowerCAmelCase = None			,					_lowerCAmelCase = None			,					_lowerCAmelCase = None			,					_lowerCAmelCase = None			,					_lowerCAmelCase = None			,					_lowerCAmelCase = None			,					_lowerCAmelCase = None			,					_lowerCAmelCase = ChannelDimension.FIRST			,					**_lowerCAmelCase			,					)     ->     PIL.Image.Image:
							'''simple docstring'''






							lowercase   =				do_resize if do_resize is not None else self.do_resize
							lowercase   =				crop_pct if crop_pct is not None else self.crop_pct
							lowercase   =				resample if resample is not None else self.resample
							lowercase   =				do_rescale if do_rescale is not None else self.do_rescale
							lowercase   =				rescale_factor if rescale_factor is not None else self.rescale_factor
							lowercase   =				do_normalize if do_normalize is not None else self.do_normalize
							lowercase   =				image_mean if image_mean is not None else self.image_mean
							lowercase   =				image_std if image_std is not None else self.image_std

							lowercase   =				size if size is not None else self.size
							lowercase   =				get_size_dict(_lowerCAmelCase			,					default_to_square=_lowerCAmelCase    )

							lowercase   =				make_list_of_images(_lowerCAmelCase    )

							if not valid_images(_lowerCAmelCase    ):
													raise ValueError(
													    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
													    """torch.Tensor, tf.Tensor or jax.ndarray."""    )

							if do_resize and size is None or resample is None:
													raise ValueError("""Size and resample must be specified if do_resize is True."""    )

							if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
													raise ValueError("""crop_pct must be specified if size < 384."""    )

							if do_rescale and rescale_factor is None:
													raise ValueError("""Rescale factor must be specified if do_rescale is True."""    )

							if do_normalize and (image_mean is None or image_std is None):
													raise ValueError("""Image mean and std must be specified if do_normalize is True."""    )

							# All transformations expect numpy arrays.
							lowercase   =				[to_numpy_array(_lowerCAmelCase    ) for image in images]

							if do_resize:
													lowercase   =				[self.resize(image=_lowerCAmelCase			,					size=_lowerCAmelCase			,					crop_pct=_lowerCAmelCase			,					resample=_lowerCAmelCase    ) for image in images]

							if do_rescale:
													lowercase   =				[self.rescale(image=_lowerCAmelCase			,					scale=_lowerCAmelCase    ) for image in images]

							if do_normalize:
													lowercase   =				[self.normalize(image=_lowerCAmelCase			,					mean=_lowerCAmelCase			,					std=_lowerCAmelCase    ) for image in images]

							lowercase   =				[to_channel_dimension_format(_lowerCAmelCase			,					_lowerCAmelCase    ) for image in images]

							lowercase   =				{'pixel_values': images}
							return BatchFeature(data=_lowerCAmelCase			,					tensor_type=_lowerCAmelCase    )










"""simple docstring"""


def        UpperCAmelCase      (					a__     ,      a__ ):





						'''simple docstring'''






						lowerCAmelCase   :Tuple						=			len(a__ )
						print('The following activities are selected:' )

						# The first activity is always selected
						lowerCAmelCase   :Dict						=			0
						print(a__     ,      end=',' )

						# Consider rest of the activities
						for j in range(a__ ):
												# If this activity has start time greater than
												# or equal to the finish time of previously
												# selected activity, then select it
												if start[j] >= finish[i]:
																		print(a__     ,      end=',' )
																		lowerCAmelCase   :List[str]						=			j


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		__SCREAMING_SNAKE_CASE   =   [1, 3, 0, 5, 8, 5]
		__SCREAMING_SNAKE_CASE   =   [2, 4, 6, 7, 9, 9]
		print_max_activities(start, finish)